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Background: Understanding the factors contributing to mental well-being in youth is a public health priority. Self-reported enthusiasm for the future may be a useful indicator of well-being and has been shown to forecast social and educational success. Typically, cross-domain measures of ecological and health-related factors with relevance to public policy and programming are analyzed either in isolation or in targeted models assessing bivariate interactions. Here, we capitalize on a large provincial data set and machine learning to identify the sociodemographic, experiential, behavioral, and other health-related factors most strongly associated with levels of subjective enthusiasm for the future in a large sample of elementary and secondary school students., Objective: The aim of this study was to identify the sociodemographic, experiential, behavioral, and other health-related factors associated with enthusiasm for the future in elementary and secondary school students using machine learning., Methods: We analyzed data from 13,661 participants in the 2019 Ontario Student Drug Use and Health Survey (OSDUHS) (grades 7-12) with complete data for our primary outcome: self-reported levels of enthusiasm for the future. We used 50 variables as model predictors, including demographics, perception of school experience (i.e., school connectedness and academic performance), physical activity and quantity of sleep, substance use, and physical and mental health indicators. Models were built using a nonlinear decision tree-based machine learning algorithm called extreme gradient boosting to classify students as indicating either high or low levels of enthusiasm. Shapley additive explanations (SHAP) values were used to interpret the generated models, providing a ranking of feature importance and revealing any nonlinear or interactive effects of the input variables., Results: The top 3 contributors to higher self-rated enthusiasm for the future were higher self-rated physical health (SHAP value=0.62), feeling that one is able to discuss problems or feelings with their parents (SHAP value=0.49), and school belonging (SHAP value=0.32). Additionally, subjective social status at school was a top feature and showed nonlinear effects, with benefits to predicted enthusiasm present in the mid-to-high range of values., Conclusions: Using machine learning, we identified key factors related to self-reported enthusiasm for the future in a large sample of young students: perceived physical health, subjective school social status and connectedness, and quality of relationship with parents. A focus on perceptions of physical health and school connectedness should be considered central to improving the well-being of youth at the population level., (©Roberta M Dolling-Boreham, Akshay Mohan, Mohamed Abdelhack, Tara Elton-Marshall, Hayley A Hamilton, Angela Boak, Daniel Felsky. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 12.09.2024.)

The heterogeneity of protein-rich inclusions and its significance in neurodegeneration is poorly understood. Standard patient-derived iPSC models develop inclusions neither reproducibly nor in a reasonable time frame. Here, we developed screenable iPSC "inclusionopathy" models utilizing piggyBac or targeted transgenes to rapidly induce CNS cells that express aggregation-prone proteins at brain-like levels. Inclusions and their effects on cell survival were trackable at single-inclusion resolution. Exemplar cortical neuron α-synuclein inclusionopathy models were engineered through transgenic expression of α-synuclein mutant forms or exogenous seeding with fibrils. We identified multiple inclusion classes, including neuroprotective p62-positive inclusions versus dynamic and neurotoxic lipid-rich inclusions, both identified in patient brains. Fusion events between these inclusion subtypes altered neuronal survival. Proteome-scale α-synuclein genetic- and physical-interaction screens pinpointed candidate RNA-processing and actin-cytoskeleton-modulator proteins like RhoA whose sequestration into inclusions could enhance toxicity. These tractable CNS models should prove useful in functional genomic analysis and drug development for proteinopathies., Competing Interests: Declaration of interests V.K. is a cofounder of and senior advisor to DaCapo Brainscience and Yumanity Therapeutics, companies focused on CNS diseases. C.Y.C. and X.J. contributed to this work as employees of Yumanity Therapeutics. T.I. and Y.K. contributed to this work as employees of Nikon Corporation. I.L., A.N., J. Sandoe, and V.K. are inventors on a patent application filed by Brigham and Women’s Hospital related to the induced inclusion iPSC models., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Substance use disorders (SUDs) are pressing global public health problems. Executive functions (EFs) are prominently featured in mechanistic models of addiction. However, there remain significant gaps in our understanding of EFs in SUDs, including the dimensional relationships of EFs to underlying neural circuits, molecular biomarkers, disorder heterogeneity, and functional ability. To improve health outcomes for people with SUDs, interdisciplinary clinical, preclinical and health services research is needed to inform policies and interventions aligned with biopsychosocial models of addiction. Here, we introduce Cognitive Dysfunction in the Addictions (CDiA), an integrative team-science and translational research program, which aims to fill these knowledge gaps and facilitate research discoveries to enhance treatments for people living with SUDs., Methods: The CDiA Program comprises seven complementary interdisciplinary projects that aim to progress understanding of EF in SUDs and investigate new biological treatment approaches. The projects draw on a diverse sample of adults aged 18-60 (target N =400) seeking treatment for addiction, who are followed prospectively over one year to identify EF domains crucial to recovery. Projects 1-3 investigate SUD symptoms, brain circuits, and blood biomarkers and their associations with both EF domains (inhibition, working memory, and set-shifting) and functional outcomes (disability, quality of life). Projects 4 and 5 evaluate interventions for addiction and their impacts on EF: a clinical trial of repetitive transcranial magnetic stimulation and a preclinical study of potential new pharmacological treatments in rodents. Project 6 links EF to healthcare utilization and is supplemented with a qualitative investigation of EF-related barriers to treatment engagement for those with substance use concerns. Project 7 uses innovative whole-person modeling to integrate the multi-modal data generated across projects, applying clustering and deep learning methods to identify patient subtypes and drive future cross-disciplinary initiatives., Discussion: The CDiA program has promise to bring scientific domains together to uncover the diverse ways in which EFs are linked to SUD severity and functional recovery. These findings, supported by emerging clinical, preclinical, health service, and whole-person modeling investigations, will facilitate future discoveries about cognitive dysfunction in addiction and could enhance the future clinical care of individuals seeking treatment for SUDs., Competing Interests: COI Statement DMB received research support and in-kind equipment support for an investigator-initiated study from Brainsway Ltd. He was the site principal investigator for three sponsor-initiated studies for Brainsway Ltd. He also received in-kind equipment support from Magventure for two investigator-initiated studies. He received medication supplies for an investigator-initiated trial from Indivior. He is a scientific advisor for Sooma Medical. He is the Co-Chair of the Clinical Standards Committee of the Clinical TMS Society (unpaid). TDP and ES are listed as inventors on patents covering the use of molecules proposed to be tested in the described study. TDP also acts as the Director of Preclinical Research and Development of DAMONA Pharmaceuticals, a spin-off company from CAMH aiming at leading the licensed technology to the clinical for reduction of cognitive deficits in brain disorders. ES acts as the CSO and Founder of DAMONA Pharmaceuticals.

Reactive astrocytes are associated with Alzheimer's disease (AD), and several AD genetic risk variants are associated with genes highly expressed in astrocytes. However, the contribution of genetic risk within astrocytes to cellular processes relevant to the pathogenesis of AD remains ill-defined. Here we present a resource for studying AD genetic risk in astrocytes using a large collection of induced pluripotent stem cell (iPSC) lines from deeply phenotyped individuals with a range of neuropathological and cognitive outcomes. IPSC lines from forty-four individuals were differentiated into astrocytes followed by unbiased molecular profiling using RNA sequencing and tandem mass tag-mass spectrometry. We demonstrate the utility of this resource in examining gene- and pathway-level associations with clinical and neuropathological traits, as well as in analyzing genetic risk and resilience factors through parallel analyses of iPSC-astrocytes and brain tissue from the same individuals. Our analyses reveal that genes and pathways altered in iPSC-derived astrocytes from AD individuals are concordantly dysregulated in AD brain tissue. This includes increased prefoldin proteins, extracellular matrix factors, COPI-mediated trafficking components and reduced proteins involved in cellular respiration and fatty acid oxidation. Additionally, iPSC-derived astrocytes from individuals resilient to high AD neuropathology show elevated basal levels of interferon response proteins and increased secretion of interferon gamma. Correspondingly, higher polygenic risk scores for AD are associated with lower levels of interferon response proteins. This study establishes an experimental system that integrates genetic information with a heterogeneous set of iPSCs to identify genetic contributions to molecular pathways affecting AD risk and resilience.

Late-Onset Alzheimer's Disease (LOAD) is a heterogeneous neurodegenerative disorder with complex etiology and high heritability. Its multifactorial risk profile and large portions of unexplained heritability suggest the involvement of yet unidentified genetic risk factors. Here we describe the "whole person" genetic risk landscape of polygenic risk scores for 2218 traits in 2044 elderly individuals and test if novel eigen-PRSs derived from clustered subnetworks of single-trait PRSs can improve the prediction of LOAD diagnosis, rates of cognitive decline, and canonical LOAD neuropathology. Network analyses revealed distinct clusters of PRSs with clinical and biological interpretability. Novel eigen-PRSs (ePRS) from these clusters significantly improved LOAD-related phenotypes prediction over current state-of-the-art LOAD PRS models. Notably, an ePRS representing clusters of traits related to cholesterol levels was able to improve variance explained in a model of the brain-wide beta-amyloid burden by 1.7% (likelihood ratio test P = 9.02 × 10-7). All associations of ePRS with LOAD phenotypes were eliminated by the removal of APOE-proximal loci. However, our association analysis identified modules characterized by PRSs of high cholesterol and LOAD. We believe this is due to the influence of the APOE region from both PRSs. We found significantly higher mean SNP effects for LOAD in the intersecting APOE region SNPs. Combining genetic risk factors for vascular traits and dementia could improve current single-trait PRS models of LOAD, enhancing the use of PRS in risk stratification. Our results are catalogued for the scientific community, to aid in generating new hypotheses based on our maps of clustered PRSs and associations with LOAD-related phenotypes., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Introduction: Understanding the factors influencing vaping cessation among young people is crucial for targeted interventions. This review aimed to summarize the individual and environmental factors that predict vaping cessation related behaviours in the young population., Methods: We systematically searched five databases for studies investigating predictors of vaping cessation behaviours among young people aged 10-35 years. Studies that examined predictors of cessation of cigarettes, other tobacco products, cannabis vaping, and studies evaluating efficacy of cessation interventions were excluded. Quality in Prognosis Studies tool was used to assess risk of bias., Results: We found 24 studies analyzing predictors of intention to quit vaping (n=15), quit attempts (n=11), and vaping abstinence (n=7). Most studies had low risk of bias, except for study attrition. We identified 107 predictors and grouped them into 'probable', 'possible', 'insufficient evidence', 'probably unrelated', and 'inconsistent direction' categories. For 'probable' predictors, we found 11 for intention to quit, 8 for quit attempts and 5 for vaping abstinence. Overall, harm perception of vaping, current other tobacco products use, frequency of use, and level of nicotine dependence were common 'probable' predictors across three outcomes, with low harm perception of vaping, dual use, and poly tobacco use associated with decreased intention to quit and quit attempts in younger population (~10-19 years)., Conclusions: Predictive modelling studies investigating vaping cessation related behaviours among young people is still limited. Future research should specifically study the natural history of vaping in youth in different jurisdictions, populations, and age groups to expand our knowledge on this area., Implications: We identified and categorized predictors of intention to quit vaping, quit attempts, and vaping abstinence among young people. While the 'probable' predictors can inform public health and policymakers to plan targeted vaping cessation programs for high-risk populations, raising public harm perception of vaping and encouraging to quit other tobacco products might increase intention to quit and quit attempts among younger population. However, the 'possible', 'insufficient evidence' and 'inconsistent direction' predictors needs further testing by future prospective longitudinal research. Additionally, we emphasized the significance of appropriate study designs, conducting research across various jurisdictions, and different population groups to obtain comprehensive insights., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco.)

Approximately 40% of dementia cases could be prevented or delayed by modifiable risk factors related to lifestyle and environment. These risk factors, such as depression and vascular disease, do not affect all individuals in the same way, likely due to inter-individual differences in genetics. However, the precise nature of how genetic risk profiles interact with modifiable risk factors to affect brain health is poorly understood. Here we combine multiple data resources, including genotyping and postmortem gene expression, to map the genetic landscape of brain structure and identify 367 loci associated with cortical thickness and 13 loci associated with white matter hyperintensities (P < 5×10 -8 ), with several loci also showing a significant association with cognitive function. We show that among 220 unique genetic loci associated with cortical thickness in our genome-wide association studies (GWAS), 95 also showed evidence of interaction with depression or cardiovascular conditions. Polygenic risk scores based on our GWAS of inferior frontal thickness also interacted with hypertension in predicting executive function in the Canadian Longitudinal Study on Aging. These findings advance our understanding of the genetic underpinning of brain structure and show that genetic risk for brain and cognitive health is in part moderated by treatable mid-life factors., (© 2024. The Author(s).)

Background: Impairment of the ubiquitin-proteasome system (UPS) has been implicated in abnormal protein accumulation in Alzheimer's disease. It remains unclear if genetic variation affects the intrinsic properties of neurons that render some individuals more vulnerable to UPS impairment., Methods: Induced pluripotent stem cell (iPSC)-derived neurons were generated from over 50 genetically variant and highly characterized participants of cohorts of aging. Proteomic profiling, proteasome activity assays, and Western blotting were employed to examine neurons at baseline and in response to UPS perturbation., Results: Neurons with lower basal UPS activity were more vulnerable to tau accumulation following mild UPS inhibition. Chronic reduction in proteasome activity in human neurons induced compensatory elevation of regulatory proteins involved in proteostasis and several proteasome subunits., Discussion: These findings reveal that genetic variation influences basal UPS activity in human neurons and differentially sensitizes them to external factors perturbing the UPS, leading to the accumulation of aggregation-prone proteins such as tau., Highlights: Polygenic risk score for AD is associated with the ubiquitin-proteasome system (UPS) in neurons. Basal proteasome activity correlates with aggregation-prone protein levels in neurons. Genetic variation affects the response to proteasome inhibition in neurons. Neuronal proteasome perturbation induces an elevation in specific proteins involved in proteostasis. Low basal proteasome activity leads to enhanced tau accumulation with UPS challenge., (© 2024 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Understanding the factors that influence smoking cessation among young people is crucial for planning targeted cessation approaches. The objective of this review was to comprehensively summarize evidence for predictors of different smoking cessation related behaviors among young people from currently available systematic reviews. We searched six databases and reference lists of the included articles for studies published up to October 20, 2023. All systematic reviews summarizing predictors of intention to quit smoking, quit attempts, or smoking abstinence among people aged 10-35 years were included. We excluded reviews on effectiveness of smoking cessation intervention; smoking prevention and other smoking behaviors; cessation of other tobacco products use, dual use, and polysubstance use. We categorized the identified predictors into 5 different categories for 3 overlapping age groups. JBI critical appraisal tool and GRADE-CERqual approach were used for quality and certainty assessment respectively. A total of 11 systematic reviews were included in this study; all summarized predictors of smoking abstinence/quit attempts and two also identified predictors of intention to quit smoking. Seven reviews had satisfactory critical appraisal score and there was minimal overlapping between the reviews. We found 4 'possible' predictors of intention to quit smoking and 119 predictors of smoking abstinence/quit attempts. Most of these 119 predictors were applicable for ~10-29 years age group. We had moderate confidence on the 'probable', 'possible', 'insufficient evidence', and 'inconsistent direction' predictors and low confidence on the 'probably unrelated' factors. The 'probable' predictors include a wide variety of socio-demographic factors, nicotine dependence, mental health, attitudes, behavioral and psychological factors, peer and family related factors, and jurisdictional policies. These predictors can guide improvement of existing smoking cessation interventions or planning of new targeted intervention programs. Other predictors as well as predictors of intention to quit smoking need to be further investigated among adolescents and young adults separately., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Kundu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: The Toronto Adolescent and Youth (TAY) Cohort Study will characterize the neurobiological trajectories of psychosis spectrum symptoms, functioning, and suicidality (i.e., suicidal thoughts and behaviors) in youth seeking mental health care. Here, we present the neuroimaging and biosample component of the protocol. We also present feasibility and quality control metrics for the baseline sample collected thus far., Methods: The current study includes youths (ages 11-24 years) who were referred to child and youth mental health services within a large tertiary care center in Toronto, Ontario, Canada, with target recruitment of 1500 participants. Participants were offered the opportunity to provide any or all of the following: 1) 1-hour magnetic resonance imaging (MRI) scan (electroencephalography if ineligible for or declined MRI), 2) blood sample for genomic and proteomic data (or saliva if blood collection was declined or not feasible) and urine sample, and 3) heart rate recording to assess respiratory sinus arrhythmia., Results: Of the first 417 participants who consented to participate between May 4, 2021, and February 2, 2023, 412 agreed to participate in the imaging and biosample protocol. Of these, 334 completed imaging, 341 provided a biosample, 338 completed respiratory sinus arrhythmia, and 316 completed all 3. Following quality control, data usability was high (MRI: T1-weighted 99%, diffusion-weighted imaging 99%, arterial spin labeling 90%, resting-state functional MRI 95%, task functional MRI 90%; electroencephalography: 83%; respiratory sinus arrhythmia: 99%)., Conclusions: The high consent rates, good completion rates, and high data usability reported here demonstrate the feasibility of collecting and using brain imaging and biosamples in a large clinical cohort of youths seeking mental health care., (Copyright © 2023 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Background: Psychosis spectrum symptoms (PSSs) occur in a sizable percentage of youth and are associated with poorer cognitive performance, poorer functioning, and suicidality (i.e., suicidal thoughts and behaviors). PSSs may occur more frequently in youths already experiencing another mental illness, but the antecedents are not well known. The Toronto Adolescent and Youth (TAY) Cohort Study aims to characterize developmental trajectories in youths with mental illness and understand associations with PSSs, functioning, and suicidality., Methods: The TAY Cohort Study is a longitudinal cohort study that aims to assess 1500 youths (age 11-24 years) presenting to tertiary care. In this article, we describe the extensive diagnostic and clinical characterization of psychopathology, substance use, functioning, suicidality, and health service utilization in these youths, with follow-up every 6 months over 5 years, including early baseline data., Results: A total of 417 participants were enrolled between May 4, 2021, and February 2, 2023. Participants met diagnostic criteria for an average of 3.5 psychiatric diagnoses, most frequently anxiety and depressive disorders. Forty-nine percent of participants met a pre-established threshold for PSSs and exhibited higher rates of functional impairment, internalizing and externalizing symptoms, and suicidality than participants without PSSs., Conclusions: Initial findings from the TAY Cohort Study demonstrate the feasibility of extensive clinical phenotyping in youths who are seeking help for mental health problems. PSS prevalence is much higher than in community-based studies. Our early data support the critical need to better understand longitudinal trajectories of clinical youth cohorts in relation to psychosis risk, functioning, and suicidality., (Copyright © 2023 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Background: Both cognition and educational achievement in youths are linked to psychosis risk. One major aim of the Toronto Adolescent and Youth (TAY) Cohort Study is to characterize how cognitive and educational achievement trajectories inform the course of psychosis spectrum symptoms (PSSs), functioning, and suicidality. Here, we describe the protocol for the cognitive and educational data and early baseline data., Methods: The cognitive assessment design is consistent with youth population cohort studies, including the NIH Toolbox, Rey Auditory Verbal Learning Test, Wechsler Matrix Reasoning Task, and Little Man Task. Participants complete an educational achievement questionnaire, and report cards are requested. Completion rates, descriptive data, and differences across PSS status are reported for the first participants (N = 417) ages 11 to 24 years, who were recruited between May 4, 2021, and February 2, 2023., Results: Nearly 84% of the sample completed cognitive testing, and 88.2% completed the educational questionnaire, whereas report cards were collected for only 40.3%. Modifications to workflows were implemented to improve data collection. Participants who met criteria for PSSs demonstrated lower performance than those who did not on numerous key cognitive indices (p < .05) and also had more academic/educational problems., Conclusions: Following youths longitudinally enabled trajectory mapping and prediction based on cognitive and educational performance in relation to PSSs in treatment-seeking youths. Youths with PSSs had lower cognitive performance and worse educational outcomes than youths without PSSs. Results show the feasibility of collecting data on cognitive and educational outcomes in a cohort of youths seeking treatment related to mental illness and substance use., (Copyright © 2023 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Changes in high-affinity nicotinic acetylcholine receptors are intricately connected to neuropathology in Alzheimer's Disease (AD). Protective and cognitive-enhancing roles for the nicotinic α5 subunit have been identified, but this gene has not been closely examined in the context of human aging and dementia. Therefore, we investigate the nicotinic α5 gene CHRNA5 and the impact of relevant single nucleotide polymorphisms (SNPs) in prefrontal cortex from 922 individuals with matched genotypic and post-mortem RNA sequencing in the Religious Orders Study and Memory and Aging Project (ROS/MAP). We find that a genotype robustly linked to increased expression of CHRNA5 (rs1979905A2) predicts significantly reduced cortical β-amyloid load. Intriguingly, co-expression analysis suggests CHRNA5 has a distinct cellular expression profile compared to other nicotinic receptor genes. Consistent with this prediction, single nucleus RNA sequencing from 22 individuals reveals CHRNA5 expression is disproportionately elevated in chandelier neurons, a distinct subtype of inhibitory neuron known for its role in excitatory/inhibitory (E/I) balance. We show that chandelier neurons are enriched in amyloid-binding proteins compared to basket cells, the other major subtype of PVALB-positive interneurons. Consistent with the hypothesis that nicotinic receptors in chandelier cells normally protect against β-amyloid, cell-type proportion analysis from 549 individuals reveals these neurons show amyloid-associated vulnerability only in individuals with impaired function/trafficking of nicotinic α5-containing receptors due to homozygosity of the missense CHRNA5 SNP (rs16969968A2). Taken together, these findings suggest that CHRNA5 and its nicotinic α5 subunit exert a neuroprotective role in aging and Alzheimer's disease centered on chandelier interneurons., (© 2024. The Author(s).)

Sleep and depression have a complex, bidirectional relationship, with sleep-associated alterations in brain dynamics and structure impacting a range of symptoms and cognitive abilities. Previous work describing these relationships has provided an incomplete picture by investigating only one or two types of sleep measures, depression, or neuroimaging modalities in parallel. We analyze the correlations between brainwide neural signatures of sleep, cognition, and depression in task and resting-state data from over 30,000 individuals from the UK Biobank and Human Connectome Project. Neural signatures of insomnia and depression are negatively correlated with those of sleep duration measured by accelerometer in the task condition but positively correlated in the resting-state condition. Our results show that resting-state neural signatures of insomnia and depression resemble that of rested wakefulness. This is further supported by our finding of hypoconnectivity in task but hyperconnectivity in resting-state data in association with insomnia and depression. These observations dispute conventional assumptions about the neurofunctional manifestations of hyper- and hypo-somnia, and may explain inconsistent findings in the literature., (© 2023. The Author(s).)

Little is known regarding the polygenic underpinnings of anomalous resting-state functional connectivity (rsFC) in youth bipolar disorder (BD). The current study examined the association of polygenic risk for BD (BD-PRS) with whole-brain rsFC at the large-scale network level in youth with and without BD. 99 youth of European ancestry (56 BD, 43 healthy controls [HC]), ages 13-20 years, completed resting-state fMRI scans. BD-PRS was calculated using summary statistics from the latest adult BD genome-wide association study. Data-driven independent component analyses of the resting-state fMRI data were implemented to examine the association of BD-PRS with rsFC in the overall sample, and separately in BD and HC. In the overall sample, higher BD-PRS was associated with lower rsFC of the salience network and higher rsFC of the frontoparietal network with frontal and parietal regions. Within the BD group, higher BD-PRS was associated with higher rsFC of the default mode network with orbitofrontal cortex, and altered rsFC of the visual network with frontal and occipital regions. Within the HC group, higher BD-PRS was associated with altered rsFC of the frontoparietal network with frontal, temporal and occipital regions. In conclusion, the current study found that BD-PRS generated based on adult genetic data was associated with altered rsFC patterns of brain networks in youth. Our findings support the usefulness of BD-PRS to investigate genetically influenced neuroimaging markers of vulnerability to BD, which can be observed in youth with BD early in their course of illness as well as in healthy youth., Competing Interests: Conflict of interest Dr. Clement C. Zai receives an honorarium for a Medscape review on bipolar disorder genetics. Dr. Mikaela K. Dimick is the recipient of a fellowship award from the Canadian Institutes of Health Research. Dr. Benjamin I. Goldstein acknowledges his position as RBC Investments Chair in Children's Mental Health and Developmental Psychopathology at CAMH, a joint Hospital-University Chair between the University of Toronto, CAMH, and the CAMH Foundation. All other authors report no actual or potential conflict of interests., (Copyright © 2023 Elsevier B.V. and ECNP. All rights reserved.)

Childhood is a sensitive period where behavioral disturbances, determined by genetics and environmental factors including sport activity, may emerge and impact risk of mental illness in adulthood. We aimed to determine if participation in sports can mitigate genetic risk for neurodevelopmental and psychiatric disorders in youth. We analyzed 4975 unrelated European youth (ages 9-10) from the Adolescent Brain Cognitive Development Study. Our outcomes were eight Child Behavior Checklist (CBCL) scores, measured annually. Polygenic risk scores (PRSs) were calculated for 21 disorders, and sport frequency and type were summarized. PRSs and sport variables were tested for main effects and interactions against CBCL outcomes using linear models. Cross-sectionally, PRSs for attention-deficit/hyperactivity disorder and major depressive disorder were associated with increases in multiple CBCL outcomes. Participation in non-contact or team sports, as well as more frequent sport participation reduced all cross-sectional CBCL outcomes, whereas involvement in contact sports increased attention problems and rule-breaking behavior. Interactions revealed that more frequent exercise was significantly associated with less rule breaking behavior in individuals with high genetic risk for obsessive compulsive disorder. Associations with longitudinal CBCL outcomes demonstrated weaker effects. We highlight the importance of genetic context when considering sports as an intervention for early life behavioural problems., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

There is a gap in knowledge regarding the polygenic underpinnings of brain anomalies observed in youth bipolar disorder (BD). This study examined the association of a polygenic risk score for BD (BD-PRS) with grey matter structure and white matter integrity in youth with and without BD. 113 participants were included in the analyses, including 78 participants with both T1-weighted and diffusion-weighted MRI images, 32 participants with T1-weighted images only, and 3 participants with diffusion-weighted images only. BD-PRS was calculated using PRS-CS-auto and was based on independent adult genome-wide summary statistics. Vertex- and voxel-wise analyses examined the associations of BD-PRS with grey matter metrics (cortical volume [CV], cortical surface area [CSA], cortical thickness [CTh]) and fractional anisotropy [FA] in the combined sample, and separately in BD and HC. In the combined sample of participants with T1-weighted images (n = 110, 66 BD, 44 HC), higher BD-PRS was associated with smaller grey matter metrics in frontal and temporal regions. In within-group analyses, higher BD-PRS was associated with lower CTh of frontal, temporal, and fusiform gyrus in BD, and with lower CV and CSA of superior frontal gyrus in HC. In the combined sample of participants with diffusion-weighted images (n = 81, 49 BD, 32 HC), higher BD-PRS was associated with lower FA in widespread white matter regions. In summary, BD-PRS calculated based on adult genetic data was negatively associated with grey matter structure and FA in youth in regions implicated in BD, which may suggest neuroimaging markers of vulnerability to BD. Future longitudinal studies are needed to examine whether BD-PRS predicts neurodevelopmental changes in BD vs. HC and its interaction with course of illness and long-term medication use., (© 2023. Springer Nature Limited.)

Background: A critical facet of motivation is effort-based decision making, which refers to the mental processes involved in deciding whether a potential reward is worth the effort. To advance understanding of how individuals with schizophrenia and major depressive disorder utilize cost-benefit information to guide choice behavior, this study aimed to characterize individual differences in the computations associated with effort-based decision making., Methods: One hundred forty-five participants (51 with schizophrenia, 43 with depression, and 51 healthy control participants) completed the Effort Expenditure for Rewards Task, with mixed effects modeling conducted to estimate the predictors of decision making. These model-derived, subject-specific coefficients were then clustered using k-means to test for the presence of discrete transdiagnostic subgroups with different profiles of reward, probability, and cost information utilization during effort-based decision making., Results: An optimal 2-cluster solution was identified, with no significant differences in the distribution of diagnostic groups between clusters. Cluster 1 (n = 76) was characterized by overall lower information utilization during decision making than cluster 2 (n = 61). Participants in this low information utilization cluster were also significantly older and more cognitively impaired, and their utilization of reward, probability, and cost was significantly correlated with clinical amotivation, depressive symptoms, and cognitive functioning., Conclusions: Our findings revealed meaningful individual differences among participants with schizophrenia, depression, and healthy control participants in their utilization of cost-benefit information in the context of effortful decision making. These findings may provide insight into different processes associated with aberrant choice behavior and may potentially guide the identification of more individualized treatment targets for effort-based motivation deficits across disorders., (Copyright © 2023 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Cognitive impairment in the elderly features complex molecular pathophysiology extending beyond the hallmark pathologies of traditional disease classification. Molecular subtyping using large-scale -omic strategies can help resolve this biological heterogeneity. Using quantitative mass spectrometry, we measured ∼8000 proteins across >600 dorsolateral prefrontal cortex tissues with clinical diagnoses of no cognitive impairment (NCI), mild cognitive impairment (MCI), and Alzheimer's disease (AD) dementia. Unbiased classification of MCI and AD cases based on individual proteomic profiles resolved three classes with expression differences across numerous cell types and biological ontologies. Two classes displayed molecular signatures atypical of AD neurodegeneration, such as elevated synaptic and decreased inflammatory markers. In one class, these atypical proteomic features were associated with clinical and pathological hallmarks of cognitive resilience. We were able to replicate these classes and their clinicopathological phenotypes across two additional tissue cohorts. These results promise to better define the molecular heterogeneity of cognitive impairment and meaningfully impact its diagnostic and therapeutic precision., Competing Interests: Declaration of Competing Interest A.I.L, N.T.S., and D.M.D. are co-founders of Emtherapro Inc. The authors declare no conflicts of interest., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

The growing global burden of mental illness has prompted calls for innovative research strategies. Theoretical models of mental health include complex contributions of biological, psychosocial, experiential, and other environmental influences. Accordingly, neuropsychiatric research has self-organized into largely isolated disciplines working to decode each individual contribution. However, research directly modeling objective biological measurements in combination with cognitive, psychological, demographic, or other environmental measurements is only now beginning to proliferate. This review aims to (1) to describe the landscape of modern mental health research and current movement towards integrative study, (2) to provide a concrete framework for quantitative integrative research, which we call Whole Person Modeling, (3) to explore existing and emerging techniques and methods used in Whole Person Modeling, and (4) to discuss our observations about the scarcity, potential value, and untested aspects of highly transdisciplinary research in general. Whole Person Modeling studies have the potential to provide a better understanding of multilevel phenomena, deliver more accurate diagnostic and prognostic tests to aid in clinical decision making, and test long standing theoretical models of mental illness. Some current barriers to progress include challenges with interdisciplinary communication and collaboration, systemic cultural barriers to transdisciplinary career paths, technical challenges in model specification, bias, and data harmonization, and gaps in transdisciplinary educational programs. We hope to ease anxiety in the field surrounding the often mysterious and intimidating world of transdisciplinary, data-driven mental health research and provide a useful orientation for students or highly specialized researchers who are new to this area., (© 2023. The Author(s).)

Many neuropsychiatric disorders are characterised by altered cortical thickness, but the cell types underlying these changes remain largely unknown. Virtual histology (VH) approaches map regional patterns of gene expression with regional patterns of MRI-derived phenotypes, such as cortical thickness, to identify cell types associated with case-control differences in those MRI measures. However, this method does not incorporate valuable information of case-control differences in cell type abundance. We developed a novel method, termed case-control virtual histology (CCVH), and applied it to Alzheimer's disease (AD) and dementia cohorts. Leveraging a multi-region gene expression dataset of AD cases (n = 40) and controls (n = 20), we quantified AD case-control differential expression of cell type-specific markers across 13 brain regions. We then correlated these expression effects with MRI-derived AD case-control cortical thickness differences across the same regions. Cell types with spatially concordant AD-related effects were identified through resampling marker correlation coefficients. Among regions thinner in AD, gene expression patterns identified by CCVH suggested fewer excitatory and inhibitory neurons, and greater proportions of astrocytes, microglia, oligodendrocytes, oligodendrocyte precursor cells, and endothelial cells in AD cases vs. controls. In contrast, original VH identified expression patterns suggesting that excitatory but not inhibitory neuron abundance was associated with thinner cortex in AD, despite the fact that both types of neurons are known to be lost in the disorder. Compared to original VH, cell types identified through CCVH are more likely to directly underlie cortical thickness differences in AD. Sensitivity analyses suggest our results are largely robust to specific analysis choices, like numbers of cell type-specific marker genes used and background gene sets used to construct null models. As more multi-region brain expression datasets become available, CCVH will be useful for identifying the cellular correlates of cortical thickness across neuropsychiatric illnesses., Competing Interests: Declaration of Competing Interest The authors declare no competing financial interests., (Copyright © 2023. Published by Elsevier Inc.)

Microglia are cells with diverse roles, including the regulation of neuronal excitability. We leveraged Patch-seq to assess the presence and effects of microglia in the local microenvironment of recorded neurons. We first quantified the amounts of microglial transcripts in three Patch-seq datasets of human and mouse neocortical neurons, observing extensive contamination. Variation in microglial contamination was explained foremost by donor identity, particularly in human samples, and additionally by neuronal cell type identity in mice. Gene set enrichment analysis suggests that microglial contamination is reflective of activated microglia, and that these transcriptional signatures are distinct from those captured via single-nucleus RNA-seq. Finally, neurons with greater microglial contamination differed markedly in their electrophysiological characteristics, including lowered input resistances and more depolarized action potential thresholds. Our results generalize beyond Patch-seq to suggest that activated microglia may be widely present across brain slice preparations and contribute to neuron- and donor-related electrophysiological variability in vitro ., Competing Interests: The authors declare no competing interests., (© 2023 The Authors.)

Age-related declines in cognitive function are driven by cell type-specific changes in the brain. However, it remains challenging to study cellular differences associated with healthy aging as traditional approaches scale poorly to the sample sizes needed to capture aging and cellular heterogeneity. Here, we employed cellular deconvolution to estimate relative cell type proportions using frontal cortex bulk gene expression from individuals without psychiatric conditions or brain pathologies. Our analyses comprised 8 datasets and 6 cohorts (1142 subjects and 1429 samples) with ages of death spanning 15-90 years. We found aging associated with profound differences in cellular proportions, with the largest changes reflecting fewer somatostatin- and vasoactive intestinal peptide-expressing interneurons, more astrocytes and other non-neuronal cells, and a suggestive "U-shaped" quadratic relationship for microglia. Cell type associations with age were markedly robust across bulk-and single nucleus datasets. Altogether, we present a comprehensive account of proportional differences in cortical cell types associated with healthy aging., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Molecular subtyping of brain tissue provides insights into the heterogeneity of common neurodegenerative conditions, such as Alzheimer's disease. However, existing subtyping studies have mostly focused on single data modalities and only those individuals with severe cognitive impairment. To address these gaps, we applied similarity network fusion, a method capable of integrating multiple high-dimensional multi-omic data modalities simultaneously, to an elderly sample spanning the full spectrum of cognitive ageing trajectories. We analyzed human frontal cortex brain samples characterized by five omic modalities: bulk RNA sequencing (18 629 genes), DNA methylation (53 932 CpG sites), histone acetylation (26 384 peaks), proteomics (7737 proteins) and metabolomics (654 metabolites). Similarity network fusion followed by spectral clustering was used for subtype detection, and subtype numbers were determined by Eigen-gap and rotation cost statistics. Normalized mutual information determined the relative contribution of each modality to the fused network. Subtypes were characterized by associations with 13 age-related neuropathologies and cognitive decline. Fusion of all five data modalities ( n = 111) yielded two subtypes ( n S1 = 53, n S2 = 58), which were nominally associated with diffuse amyloid plaques; however, this effect was not significant after correction for multiple testing. Histone acetylation (normalized mutual information = 0.38), DNA methylation (normalized mutual information = 0.18) and RNA abundance (normalized mutual information = 0.15) contributed most strongly to this network. Secondary analysis integrating only these three modalities in a larger subsample ( n = 513) indicated support for both three- and five-subtype solutions, which had significant overlap, but showed varying degrees of internal stability and external validity. One subtype showed marked cognitive decline, which remained significant even after correcting for tests across both three- and five-subtype solutions ( p Bonf = 5.9 × 10 -3 ). Comparison to single-modality subtypes demonstrated that the three-modal subtypes were able to uniquely capture cognitive variability. Comprehensive sensitivity analyses explored influences of sample size and cluster number parameters. We identified highly integrative molecular subtypes of ageing derived from multiple high dimensional, multi-omic data modalities simultaneously. Fusing RNA abundance, DNA methylation, and histone acetylation measures generated subtypes that were associated with cognitive decline. This work highlights the potential value and challenges of multi-omic integration in unsupervised subtyping of post-mortem brain., Competing Interests: The authors report no competing interests., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Background: Our understanding of major depression is complicated by substantial heterogeneity in disease presentation, which can be disentangled by data-driven analyses of depressive symptom dimensions. We aimed to determine the clinical portrait of such symptom dimensions among individuals in the community., Methods: This cross-sectional study consisted of 25 261 self-reported White UK Biobank participants with major depression. Nine questions from the UK Biobank Mental Health Questionnaire encompassing depressive symptoms were decomposed into underlying factors or 'symptom dimensions' via factor analysis, which were then tested for association with psychiatric diagnoses and polygenic risk scores for major depressive disorder (MDD), bipolar disorder and schizophrenia. Replication was performed among 655 self-reported non-White participants, across sexes, and among 7190 individuals with an ICD-10 code for MDD from linked inpatient or primary care records., Results: Four broad symptom dimensions were identified, encompassing negative cognition, functional impairment, insomnia and atypical symptoms. These dimensions replicated across ancestries, sexes and individuals with inpatient or primary care MDD diagnoses, and were also consistent among 43 090 self-reported White participants with undiagnosed self-reported depression. Every dimension was associated with increased risk of nearly every psychiatric diagnosis and polygenic risk score. However, while certain psychiatric diagnoses were disproportionately associated with specific symptom dimensions, the three polygenic risk scores did not show the same specificity of associations., Conclusions: An analysis of questionnaire data from a large community-based cohort reveals four replicable symptom dimensions of depression with distinct clinical, but not genetic, correlates.

Identifying ancestry-specific molecular profiles of late-onset Alzheimer's Disease (LOAD) in brain tissue is crucial to understand novel mechanisms and develop effective interventions in non-European, high-risk populations. We performed gene differential expression (DE) and consensus network-based analyses in RNA-sequencing data of postmortem brain tissue from 39 Caribbean Hispanics (CH). To identify ancestry-concordant and -discordant expression profiles, we compared our results to those from two independent non-Hispanic White (NHW) samples (n = 731). In CH, we identified 2802 significant DE genes, including several LOAD known-loci. DE effects were highly concordant across ethnicities, with 373 genes transcriptome-wide significant in all three cohorts. Cross-ancestry meta-analysis found NPNT to be the top DE gene. We replicated over 82% of meta-analyses genome-wide signals in single-nucleus RNA-seq data (including NPNT and LOAD known-genes SORL1, FBXL7, CLU, ABCA7). Increasing representation in genetic studies will allow for deeper understanding of ancestry-specific mechanisms and improving precision treatment options in understudied groups., Competing Interests: Declaration of Competing Interest Leon French owns shares in Cortexyme Inc., a company that is developing a gingipain inhibitor to treat Alzheimer's Disease. The other authors declare no conflict of interest. Funders did not play any role in the design, analysis, or writing or this study., (Copyright © 2022. Published by Elsevier Inc.)

Background: The bidirectional relationship between sleep disturbances and depression is well documented, yet the biology of sleep is not fully understood. Mitochondria have become of interest not only because of the connection between sleep and metabolism but also because of mitochondria's involvement in the production of reactive oxygen species, which are largely scavenged during sleep., Methods: Genome-wide association studies (GWAS) of eight accelerometry-derived sleep measures were performed across both the autosomal and mitochondrial DNA (mtDNA) among two severity levels of depression in UK Biobank participants. We calculated SNP heritability for each of the sleep measures. Linear regression was performed to test associations and mitochondrial haplogroups., Results: Variants included in the GWAS accounted for moderate heritability of bedtime (19.6%, p = 4.75 × 10 -7 ), sleep duration (16.6%, p = 8.58 × 10 -6 ) and duration of longest sleep bout (22.6%, p = 4.64 × 10 -4 ). No variants passed genome-wide significance in the autosomal genome. The top hit in the severe depression sample was rs145019802, near GOLGA8B, for sleep efficiency (p = 1.17 × 10 -7 ), and the top hit in the broad depression sample was rs7100859, an intergenic SNP, and nap duration (p = 1.25 × 10 -7 ). Top mtDNA loci were m.12633C > A of MT-ND5 with bedtime (p = 0.002) in the severe sample and m.16186C > T of the control region with nap duration (p = 0.002) in the broad sample., Conclusion: SNP heritability estimates support the involvement of common SNPs in specific sleep measures among depressed individuals. This is the first study to analyze mtDNA variance in sleep measures in depressed individuals. Our mtDNA findings, although nominally significant, provide preliminary suggestion that mitochondria are involved in sleep., Competing Interests: Declaration of competing interest The authors have no competing interests to disclose., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background: Sex is seldom considered as a potential moderator of the impact of bipolar disorder (BD) on cardiovascular disease (CVD) risk. We aimed to characterize the sex-specific association of CVD and BD using data from the UK Biobank., Methods: In a cross-sectional analysis, we compared the odds ratio between women and men with BD for seven CVD diagnoses (coronary artery disease, myocardial infarction, angina, atrial fibrillation, heart failure, stroke, and essential hypertension) and four cardiovascular biomarkers (arterial stiffness index, low-density lipoprotein, C-reactive protein, and HbA1c) in 293 participants with BD and 257,380 psychiatrically healthy controls in the UK Biobank., Results: After adjusting for age, we found a two- to three-fold stronger association among women than among men between BD and rates of coronary artery disease, heart failure, and essential hypertension, with a significant sex-by-diagnosis interactions. The association remained significant after controlling for self-reported race, education, income, and smoking status. After controlling for potential confounders, there was no significant association between sex and any cardiovascular biomarkers., Limitations: These analyses could not disentangle effects of BD from its treatment., Conclusions: Our results underscore the importance of incorporating sex and mental illness in risk estimation tools for CVD, and improving screening for, and timely treatment of, CVD in those with BD. Future research is needed to better understand the contributors and mechanisms of sex differences related to CVD risk in BD., Competing Interests: Conflict of Interest AO's work is supported by the Canadian Institutes of Health Research (CIHR) and the US National Institutes of Mental Health (NIMH). BM holds and receives support from the Labatt Family Chair in Biology of Depression in Late-Life Adults at the University of Toronto. He currently receives research support from Brain Canada, CIHR, the CAMH Foundation, the Patient-Centered Outcomes Research Institute (PCORI), and NIH; during the past three years, he has received non-financial support from Eli Lilly (medications and matching placebo for a NIH-funded clinical trial), Pfizer (medications for a NIH-funded clinical trial), Capital Solution Design LLC (software used in a study funded by the CAMH Foundation), and HAPPYneuron (software used in a study founded by Brain Canada). This work was supported in part by Academic Scholars Awards from the Department of Psychiatry, University of Toronto (AO) and by the Labatt Family Chair in Biology of Depression in Late-Life Adults at the University of Toronto (BHM)., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Identifying genes underlying memory function will help characterize cognitively resilient and high-risk declining subpopulations contributing to precision medicine strategies. We estimated episodic memory trajectories in 35,245 ethnically diverse older adults representing eight independent cohorts. We conducted apolipoprotein E (APOE)-stratified genome-wide association study (GWAS) analyses and combined individual cohorts' results via meta-analysis. Three independent transcriptomics datasets were used to further interpret GWAS signals. We identified DCDC2 gene significantly associated with episodic memory (Pmeta = 3.3 x 10 -8 ) among non-carriers of APOE ε4 (N = 24,941). Brain transcriptomics revealed an association between episodic memory maintenance and (1) increased dorsolateral prefrontal cortex DCDC2 expression (P = 3.8 x 10 -4 ) and (2) lower burden of pathological Alzheimer's disease (AD) hallmarks (paired helical fragment tau P = .003, and amyloid beta load P = .008). Additional transcriptomics results comparing AD and cognitively healthy brain samples showed a downregulation of DCDC2 levels in superior temporal gyrus (P = .007) and inferior frontal gyrus (P = .013). Our work identified DCDC2 gene as a novel predictor of memory maintenance., (© 2021 the Alzheimer's Association.)

Cortical neuron loss is a pathological hallmark of late-onset Alzheimer's disease (AD). However, it remains unclear which neuronal subtypes beyond broad excitatory and inhibitory classes are most vulnerable. Here, we analyzed cell subtype proportion differences in AD compared to non-AD controls using 1037 post-mortem brain samples from six neocortical regions. We identified the strongest associations of AD with fewer somatostatin (SST) inhibitory neurons (β = -0.48, p bonf = 8.98 × 10 -9 ) and intra-telencephalic (IT) excitatory neurons (β = -0.45, p bonf = 4.32 × 10 -7 ). Replication in three AD case-control single-nucleus RNAseq datasets most strongly supported the bulk tissue association of fewer SST neurons in AD. In depth analyses of cell type proportions with specific AD-related neuropathological and cognitive phenotypes revealed fewer SST neurons with greater brain-wide post-mortem tau and beta amyloid, as well as a faster rate of antemortem cognitive decline. In contrast, greater IT neuron proportions were associated with a slower rate of cognitive decline as well as greater residual cognition-a measure of cognitive resilience-but not canonical AD neuropathology. Our findings implicate somatostatin inhibitory and intra-telencephalic excitatory neuron subclasses in the pathogenesis of AD and in cognitive resilience to AD pathology, respectively., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Consens, Chen, Menon, Wang, Schneider, De Jager, Bennett, Tripathy and Felsky.)

Alpha-synuclein (αS) is a conformationally plastic protein that reversibly binds to cellular membranes. It aggregates and is genetically linked to Parkinson's disease (PD). Here, we show that αS directly modulates processing bodies (P-bodies), membraneless organelles that function in mRNA turnover and storage. The N terminus of αS, but not other synucleins, dictates mutually exclusive binding either to cellular membranes or to P-bodies in the cytosol. αS associates with multiple decapping proteins in close proximity on the Edc4 scaffold. As αS pathologically accumulates, aberrant interaction with Edc4 occurs at the expense of physiologic decapping-module interactions. mRNA decay kinetics within PD-relevant pathways are correspondingly disrupted in PD patient neurons and brain. Genetic modulation of P-body components alters αS toxicity, and human genetic analysis lends support to the disease-relevance of these interactions. Beyond revealing an unexpected aspect of αS function and pathology, our data highlight the versatility of conformationally plastic proteins with high intrinsic disorder., Competing Interests: Declaration of interests V.K. is a co-founder of and senior advisor to DaCapo Brainscience and Yumanity Therapeutics, companies focused on CNS diseases. C.Y.C. and X.J. contributed to this work as employees of Yumanity Therapeutics., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

The extent of shared and distinct neural mechanisms underlying major depressive disorder (MDD), anxiety, and stress-related disorders is still unclear. We compared the neural signatures of these disorders in 5,405 UK Biobank patients and 21,727 healthy controls. We found the greatest case–control differences in resting-state functional connectivity and cortical thickness in MDD, followed by anxiety and stress-related disorders. Neural signatures for MDD and anxiety disorders were highly concordant, whereas stress-related disorders showed a distinct pattern. Controlling for cross-disorder genetic risk somewhat decreased the similarity between functional neural signatures of stress-related disorders and both MDD and anxiety disorders. Among cases and healthy controls, reduced within-network and increased between-network frontoparietal and default mode connectivity were associated with poorer cognitive performance (processing speed, attention, associative learning, and fluid intelligence). These results provide evidence for distinct neural circuit function impairments in MDD and anxiety disorders compared to stress disorders, yet cognitive impairment appears unrelated to diagnosis and varies with circuit function.

Background: Mental health problems and substance use co-morbidities during and after the COVID-19 pandemic are a public health priority. Identifying individuals at high-risk of developing mental health problems and potential sequela can inform mitigating strategies. We aimed to identify distinct groups of individuals (i.e., latent classes) based on patterns of self-reported mental health symptoms and investigate their associations with alcohol and cannabis use., Methods: We used data from six successive waves of a web-based cross-sectional survey of adults aged 18 years and older living in Canada (6,021 participants). We applied latent class analysis to three domains of self-reported mental health most likely linked to effects of the pandemic: anxiety, depression, and loneliness. Logistic regression was used to characterize latent class membership, estimate the association of class membership with alcohol and cannabis use, and perform sex-based analyses., Results: We identified two distinct classes: (1) individuals with low scores on all three mental health indicators (no/low-symptoms) and (2) those reporting high scores across the three measures (high-symptoms). Between 73.9 and 77.1% of participants were in the no/low-symptoms class and 22.9-26.1% of participants were in the high-symptom class. We consistently found across all six waves that individuals at greater risk of being in the high-symptom class were more likely to report worrying about getting COVID-19 with adjusted odds ratios (aORs) between 1.72 (95%CI:1.17-2.51) and 3.51 (95%CI:2.20-5.60). Those aged 60 + were less likely to be in this group with aORs (95%CI) between 0.26 (0.15-0.44) and 0.48 (0.29-0.77) across waves. We also found some factors associated with class membership varied at different time points. Individuals in the high-symptom class were more likely to use cannabis at least once a week (aOR = 2.28, 95%CI:1.92-2.70), drink alcohol heavily (aOR = 1.71, 95%CI:1.49-1.96); and increase the use of cannabis (aOR = 3.50, 95%CI:2.80-4.37) and alcohol (aOR = 2.37, 95%CI:2.06-2.74) during the pandemic. Women in the high-symptom class had lower odds of drinking more alcohol during the pandemic than men., Conclusions: We identified the determinants of experiencing high anxiety, depression, and loneliness symptoms and found a significant association with alcohol and cannabis consumption. This suggests that initiatives and supports are needed to address mental health and substance use multi-morbidities., (© 2022. The Author(s).)

Identifying the effects of genetic variation on the epigenome in disease-relevant cell types can help advance our understanding of the first molecular contributions of genetic susceptibility to disease onset. Here, we establish a genome-wide map of DNA methylation quantitative trait loci in CD4 + T-cells isolated from multiple sclerosis patients. Utilizing this map in a colocalization analysis, we identify 19 loci where the same haplotype drives both multiple sclerosis susceptibility and local DNA methylation. We also identify two distant methylation effects of multiple sclerosis susceptibility loci: a chromosome 16 locus affects PRDM8 methylation (a chromosome 4 region not previously associated with multiple sclerosis), and the aggregate effect of multiple sclerosis-associated variants in the major histocompatibility complex influences DNA methylation near PRKCA (chromosome 17). Overall, we present a new resource for a key cell type in inflammatory disease research and uncover new gene targets for the study of predisposition to multiple sclerosis., (© 2021. The Author(s).)

RNA editing is a feature of RNA maturation resulting in the formation of transcripts whose sequence differs from the genome template. Brain RNA editing may be altered in Alzheimer's disease (AD). Here, we analyzed data from 1,865 brain samples covering 9 brain regions from 1,074 unrelated subjects on a transcriptome-wide scale to identify inter-regional differences in RNA editing. We expand the list of known brain editing events by identifying 58,761 previously unreported events. We note that only a small proportion of these editing events are found at the protein level in our proteome-wide validation effort. We also identified the occurrence of editing events associated with AD dementia, neuropathological measures and longitudinal cognitive decline in: SYT11, MCUR1, SOD2, ORAI2, HSDL2, PFKP, and GPRC5B. Thus, we present an extended reference set of brain RNA editing events, identify a subset that are found to be expressed at the protein level, and extend the narrative of transcriptomic perturbation in AD to RNA editing., (© 2021. The Author(s).)

Background: The COVID-19 global pandemic has increased the burden of mental illness on Canadian adults. However, the complex combination of demographic, economic, and lifestyle factors and perceived health risks contributing to patterns of anxiety and depression has not been explored., Objective: The aim of this study is to harness flexible machine learning methods to identify constellations of factors related to symptoms of mental illness and to understand their changes over time during the COVID-19 pandemic., Methods: Cross-sectional samples of Canadian adults (aged ≥18 years) completed web-based surveys in 6 waves from May to December 2020 (N=6021), and quota sampling strategies were used to match the English-speaking Canadian population in age, gender, and region. The surveys measured anxiety and depression symptoms, sociodemographic characteristics, substance use, and perceived COVID-19 risks and worries. First, principal component analysis was used to condense highly comorbid anxiety and depression symptoms into a single data-driven measure of emotional distress. Second, eXtreme Gradient Boosting (XGBoost), a machine learning algorithm that can model nonlinear and interactive relationships, was used to regress this measure on all included explanatory variables. Variable importance and effects across time were explored using SHapley Additive exPlanations (SHAP)., Results: Principal component analysis of responses to 9 anxiety and depression questions on an ordinal scale revealed a primary latent factor, termed "emotional distress," that explained 76% of the variation in all 9 measures. Our XGBoost model explained a substantial proportion of variance in emotional distress (r 2 =0.39). The 3 most important items predicting elevated emotional distress were increased worries about finances (SHAP=0.17), worries about getting COVID-19 (SHAP=0.17), and younger age (SHAP=0.13). Hopefulness was associated with emotional distress and moderated the impacts of several other factors. Predicted emotional distress exhibited a nonlinear pattern over time, with the highest predicted symptoms in May and November and the lowest in June., Conclusions: Our results highlight factors that may exacerbate emotional distress during the current pandemic and possible future pandemics, including a role of hopefulness in moderating distressing effects of other factors. The pandemic disproportionately affected emotional distress among younger adults and those economically impacted., (©Katrina Hueniken, Nibene Habib Somé, Mohamed Abdelhack, Graham Taylor, Tara Elton Marshall, Christine M Wickens, Hayley A Hamilton, Samantha Wells, Daniel Felsky. Originally published in JMIR Mental Health (https://mental.jmir.org), 17.11.2021.)

We have generated a controlled and manipulable resource that captures genetic risk for Alzheimer's disease: iPSC lines from 53 individuals coupled with RNA and proteomic profiling of both iPSC-derived neurons and brain tissue of the same individuals. Data collected for each person include genome sequencing, longitudinal cognitive scores, and quantitative neuropathology. The utility of this resource is exemplified here by analyses of neurons derived from these lines, revealing significant associations between specific Aβ and tau species and the levels of plaque and tangle deposition in the brain and, more importantly, with the trajectory of cognitive decline. Proteins and networks are identified that are associated with AD phenotypes in iPSC neurons, and relevant associations are validated in brain. The data presented establish this iPSC collection as a resource for investigating person-specific processes in the brain that can aid in identifying and validating molecular pathways underlying AD., Competing Interests: Declaration of interests D.J.S. is a director and consultant for Prothena Biosciences., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: Sleep problems are both symptoms of and modifiable risk factors for many psychiatric disorders. Wrist-worn accelerometers enable objective measurement of sleep at scale. Here, we aimed to examine the association of accelerometer-derived sleep measures with psychiatric diagnoses and polygenic risk scores in a large community-based cohort., Methods and Findings: In this post hoc cross-sectional analysis of the UK Biobank cohort, 10 interpretable sleep measures-bedtime, wake-up time, sleep duration, wake after sleep onset, sleep efficiency, number of awakenings, duration of longest sleep bout, number of naps, and variability in bedtime and sleep duration-were derived from 7-day accelerometry recordings across 89,205 participants (aged 43 to 79, 56% female, 97% self-reported white) taken between 2013 and 2015. These measures were examined for association with lifetime inpatient diagnoses of major depressive disorder, anxiety disorders, bipolar disorder/mania, and schizophrenia spectrum disorders from any time before the date of accelerometry, as well as polygenic risk scores for major depression, bipolar disorder, and schizophrenia. Covariates consisted of age and season at the time of the accelerometry recording, sex, Townsend deprivation index (an indicator of socioeconomic status), and the top 10 genotype principal components. We found that sleep pattern differences were ubiquitous across diagnoses: each diagnosis was associated with a median of 8.5 of the 10 accelerometer-derived sleep measures, with measures of sleep quality (for instance, sleep efficiency) generally more affected than mere sleep duration. Effect sizes were generally small: for instance, the largest magnitude effect size across the 4 diagnoses was β = -0.11 (95% confidence interval -0.13 to -0.10, p = 3 × 10-56, FDR = 6 × 10-55) for the association between lifetime inpatient major depressive disorder diagnosis and sleep efficiency. Associations largely replicated across ancestries and sexes, and accelerometry-derived measures were concordant with self-reported sleep properties. Limitations include the use of accelerometer-based sleep measurement and the time lag between psychiatric diagnoses and accelerometry., Conclusions: In this study, we observed that sleep pattern differences are a transdiagnostic feature of individuals with lifetime mental illness, suggesting that they should be considered regardless of diagnosis. Accelerometry provides a scalable way to objectively measure sleep properties in psychiatric clinical research and practice, even across tens of thousands of individuals., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: M.A.R. is on the SAB of 54Gene, Related Sciences and scientific founder of Broadwing Bio and has advised BioMarin, Third Rock Ventures and MazeTx; the remaining authors declare no competing interests.

Objective: Polygenic risk scores (PRSs) assess the individual genetic propensity to a condition by combining sparse information scattered across genetic loci, often displaying small effect sizes. Most PRSs are constructed in European-ancestry populations, limiting their use in other ethnicities. Here we constructed and validated a PRS for late-onset Alzheimer's Disease (LOAD) in Caribbean Hispanics (CH)., Methods: We used a CH discovery (n = 4,312) and independent validation sample (n = 1,850) to construct an ancestry-specific PRS ("CH-PRS") and evaluated its performance alone and with other predictors using the area under curve (AUC) and logistic regression (strength of association with LOAD and statistical significance). We tested if CH-PRS predicted conversion to LOAD in a subsample with longitudinal data (n = 1,239). We also tested the CH-PRS in an independent replication CH cohort (n = 200) and brain autopsy cohort (n = 33). Finally, we tested the effect of ancestry on PRS by using European and African American discovery cohorts to construct alternative PRSs ("EUR-PRS", "AA-PRS")., Results: The full model (LOAD ~ CH-PRS + sex + age + APOE-ɛ4), achieved an AUC = 74% (OR CH-PRS  = 1.51 95%CI = 1.36-1.68), raising to >75% in APOE-ɛ4 non-carriers. CH-PRS alone achieved an AUC = 72% in the autopsy cohort, raising to AUC = 83% in full model. Higher CH-PRS was significantly associated with clinical LOAD in the replication CH cohort (OR = 1.61, 95%CI = 1.19-2.17) and significantly predicted conversion to LOAD (HR = 1.93, CI = 1.70-2.20) in the longitudinal subsample. EUR-PRS and AA-PRS reached lower prediction accuracy (AUC = 58% and 53%, respectively)., Interpretation: Enriching diversity in genetic studies is critical to provide an effective PRS in profiling LOAD risk across populations. ANN NEUROL 2021;90:366-376., (© 2021 American Neurological Association.)

Prevention of major depressive disorder (MDD) is a public health priority. Identifying biomarkers of underlying biological processes that contribute to MDD onset may help address this public health need. This prospective cohort study encompassed 383,131 white British participants from the UK Biobank with no prior history of MDD, with replication in 50,759 participants of other ancestries. Leveraging linked inpatient and primary care records, we computed adjusted odds ratios for 5-year MDD incidence among individuals with values below or above the 95% confidence interval (<2.5th or >97.5th percentile) on each of 57 laboratory measures. Sensitivity analyses were performed across multiple percentile thresholds and in comparison to established reference ranges. We found that indicators of liver dysfunction were associated with increased 5-year MDD incidence (even after correction for alcohol use and body mass index): elevated alanine aminotransferase (AOR = 1.35, 95% confidence interval [1.16, 1.58]), aspartate aminotransferase (AOR = 1.39 [1.19, 1.62]), and gamma glutamyltransferase (AOR = 1.52 [1.31, 1.76]) as well as low albumin (AOR = 1.28 [1.09, 1.50]). Similar observations were made with respect to endocrine dysregulation, specifically low insulin-like growth factor 1 (AOR = 1.34 [1.16, 1.55]), low testosterone among males (AOR = 1.60 [1.27, 2.00]), and elevated glycated hemoglobin (HbA1C; AOR = 1.23 [1.05, 1.43]). Markers of renal impairment (i.e. elevated cystatin C, phosphate, and urea) and indicators of anemia and macrocytosis (i.e. red blood cell enlargement) were also associated with MDD incidence. While some immune markers, like elevated white blood cell and neutrophil count, were associated with MDD (AOR = 1.23 [1.07, 1.42]), others, like elevated C-reactive protein, were not (AOR = 1.04 [0.89, 1.22]). The 30 significant associations validated as a group in the multi-ancestry replication cohort (Wilcoxon p = 0.0005), with a median AOR of 1.235. Importantly, all 30 significant associations with extreme laboratory test results were directionally consistent with an increased MDD risk. In sum, markers of liver and kidney dysfunction, growth hormone and testosterone deficiency, innate immunity, anemia, macrocytosis, and insulin resistance were associated with MDD incidence in a large community-based cohort. Our results support a contributory role of diverse biological processes to MDD onset.